from cascadeflow import HarnessConfig

config = HarnessConfig(
    mode="enforce",                     # "off" | "observe" | "enforce"
    verbose=False,                      # Print decisions to stderr

    # Hard constraints
    budget=0.50,                        # Max USD for the run
    max_tool_calls=10,                  # Max tool/function calls
    max_latency_ms=5000.0,              # Max wall-clock ms per call
    max_energy=100.0,                   # Max energy units

    # Soft scoring
    kpi_weights={                       # Relative importance
        "quality": 0.6,
        "cost": 0.3,
        "latency": 0.1,
    },
    kpi_targets={"quality": 0.9},       # Target values for KPI dimensions

    # Compliance
    compliance="gdpr",                  # "gdpr" | "hipaa" | "pci" | "strict"
)

import type { HarnessConfig } from '@cascadeflow/core';

const config: HarnessConfig = {
  mode: 'enforce',
  verbose: false,
  budget: 0.50,
  maxToolCalls: 10,
  maxLatencyMs: 5000,
  maxEnergy: 100,
  kpiWeights: { quality: 0.6, cost: 0.3, latency: 0.1 },
  compliance: 'regulated',
};

import cascadeflow
cascadeflow.init(mode="observe")
# All LLM calls are tracked. Nothing changes.

import { init } from '@cascadeflow/core';

init({ mode: 'observe' });
// Instrumented OpenAI and Anthropic SDK calls are tracked.

cascadeflow.init(mode="enforce")

with cascadeflow.run(budget=0.50, compliance="gdpr") as session:
    result = await agent.run("Analyze EU data")
    print(session.summary())

import { init, run } from '@cascadeflow/core';

init({ mode: 'enforce' });

await run({ budget: 0.50, compliance: 'regulated' }, async (session) => {
  await agent.run('Analyze regulated data');
  console.log(session.summary());
});

@cascadeflow.agent(
    budget=1.00,
    compliance="hipaa",
    kpi_weights={"quality": 0.8, "cost": 0.2},
)
async def medical_agent(query: str):
    return await llm.complete(query)

import { harnessAgent } from '@cascadeflow/core';

const medicalAgent = harnessAgent({
  budget: 1.00,
  compliance: 'regulated',
  kpiWeights: { quality: 0.8, cost: 0.2 },
})(async (query: string) => llm.complete(query));